EXTREME QUANTILE REGRESSION IN A PROPORTIONAL TAIL FRAMEWORK
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | EXTREME QUANTILE REGRESSION IN A PROPORTIONAL TAIL FRAMEWORK |
| Type de publication | Journal Article |
| Year of Publication | 2021 |
| Auteurs | Bobbia B, Dombry C, Varron D |
| Journal | TRANSACTIONS OF A RAZMADZE MATHEMATICAL INSTITUTE |
| Volume | 175 |
| Pagination | 13-32 |
| Date Published | APR |
| Type of Article | Article |
| ISSN | 2346-8092 |
| Mots-clés | Coupling methods, empirical processes, Extreme value theory |
| Résumé | The model of heteroscedastic extremes initially introduced by Einmahl et al. (JRSSB, 2016) describes the evolution of a nonstationary sequence whose extremes evolve over time. We revisit this model and adapt it into a general extreme quantile regression framework. We provide estimates for the extreme value index and the integrated skedasis function and prove their joint asymptotic normality. Our results are quite similar to those developed for heteroscedastic extremes, but with a different proof approach emphasizing coupling arguments. We also propose a pointwise estimator of the skedasis function and a Weissman estimator of conditional extreme quantiles and prove the asymptotic normality of both estimators. |